The Study of anti-inflammatory Mechanism with Cobra Venom on Astrocytes of Rats (뇌(腦) 성상세포(星狀細胞)를 대상으로 한 Cobrotoxin의 염증(炎症) 치료(治療) 기전(機轉) 연구(硏究))
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- Journal of Acupuncture Research
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- v.22 no.3
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- pp.155-167
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- 2005
Objectives : The purpose of this study was to investigate the anti-inflammatory effect of Cobrotoxin on binding affinity of cobrotoxin with P50,
The objectives of the 1963 Tokyo Convention cover a variety of subjects, with the intention of providing safety in aircraft, protection of life and property on board, and promoting the security of civil aviation. These objectives will be treated as follows: first, the unification of rules on jurisdiction; second, the question of filling the gap in jurisdiction; third, the scheme of maintaining law and order on board aircraft; fourth, the protection of persons acting in accordance with the Convention; fifth, the protection of the interests of disembarked persons; sixth, the question of hijacking of aircraft; and finally some general remarks on the objectives of the Convention. The Tokyo Convention mainly deals with general crimes such as murder, violence, robbery on board aircraft rather than aviation terrorism. The Article 11 of the Convention deals with hijacking in a simple way. As far as aviation terrorism is concerned 1970 Hague Convention and 1971 Montreal Convention cover the hijacking and sabotage respectively. The Problem of national jurisdiction over the offence and the offender was as tangled at the Hague and Montreal Convention, as under the Tokyo Convention. Under the Tokyo Convention the prime base of jurisdiction is the law of the flag (Article 3), but concurrent jurisdiction is also allowed on grounds of: territorial principle, active nationality and passive personality principle, security of the state, breach of flight rules, and exercise of jurisdiction necessary for the performance of obligations under multilateral agreements (Article 4). No Criminal jurisdiction exercised in accordance with national law is excluded [Article 3(2)]. However, Article 4 of the Hague Convention(hereafter Hague Article 4) and Article 5 of the Montreal Convention(hereafter Montreal Article 5), dealing with jurisdiction have moved a step further, inasmuch as the opening part of both paragraphs 1 and 2 of the Hague Article 4 and the Montreal Article 5 impose an obligation on all contracting states to take measures to establish jurisdiction over the offence (i.e., to ensure that their law is such that their courts will have jurisdiction to try offender in all the circumstances covered by Hague Article 4 and Montreal Article 5). The state of registration and the state where the aircraft lands with the hijacker still on board will have the most interest, and would be in the best position to prosecute him; the paragraphs 1(a) and (b) of the Hague Article 4 and paragraphs 1(b) and (c) of the Montreal Article 5 deal with it, respectively. However, paragraph 1(b) of the Hague Article 4 and paragraph 1(c) of the Montreal Article 5 do not specify if the aircraft is still under the control of the hijacker or if the hijacker has been overpowered by the aircraft commander, or if the offence has at all occurred in the airspace of the state of landing. The language of the paragraph would probably cover all these cases. The weaknesses of Hague Article 4 and Montreal Article 5 are however, patent. The Jurisdictions of the state of registration, the state of landing, the state of the lessee and the state where the offender is present, are concurrent. No priorities have been fixed despite a proposal to this effect in the Legal Committee and the Diplomatic Conference, and despite the fact that it was pointed out that the difficulty in accepting the Tokyo Convention has been the question of multiple jurisdiction, for the reason that it would be too difficult to determine the priorities. Disputes over the exercise of jurisdiction can be endemic, more so when Article 8(4) of the Hague Convention and the Montreal Convention give every state mentioned in Hague Article 4(1) and Montreal Article 5(1) the right to seek extradition of the offender. A solution to the problem should not have been given up only because it was difficult. Hague Article 4(3) and Montreal Article 5(3) provide that they do not exclude any criminal jurisdiction exercised in accordance with national law. Thus the provisions of the two Conventions create additional obligations on the state, and do not exclude those already existing under national laws. Although the two Conventions do not require a state to establish jurisdiction over, for example, hijacking or sabotage committed by its own nationals in a foreign aircraft anywhere in the world, they do not preclude any contracting state from doing so. However, it has be noted that any jurisdiction established merely under the national law would not make the offence an extraditable one under Article 8 of the Hague and Montreal Convention. As far as international aviation terrorism is concerned 1988 Montreal Protocol and 1991 Convention on Marking of Plastic Explosives for the Purpose of Detention are added. The former deals with airport terrorism and the latter plastic explosives. Compared to the other International Terrorism Conventions, the International Aviation Terrorism Conventions do not have clauses of the passive personality principle. If the International Aviation Terrorism Conventions need to be revised in the future, those clauses containing the passive personality principle have to be inserted for the suppression of the international aviation terrorism more effectively. Article 3 of the 1973 Convention on the Prevention and Punishment of Crimes Against Internationally Protected Persons, Including Diplomatic Agents, Article 5 of the 1979 International Convention against the Taking of Hostages and Article 6 of the 1988 Convention for the Suppression of Unlawful Acts Against the Safety of Maritime Navigation would be models that the revised International Aviation Terrorism Conventions could follow in the future.
The data center is a physical environment facility for accommodating computer systems and related components, and is an essential foundation technology for next-generation core industries such as big data, smart factories, wearables, and smart homes. In particular, with the growth of cloud computing, the proportional expansion of the data center infrastructure is inevitable. Monitoring the health of these data center facilities is a way to maintain and manage the system and prevent failure. If a failure occurs in some elements of the facility, it may affect not only the relevant equipment but also other connected equipment, and may cause enormous damage. In particular, IT facilities are irregular due to interdependence and it is difficult to know the cause. In the previous study predicting failure in data center, failure was predicted by looking at a single server as a single state without assuming that the devices were mixed. Therefore, in this study, data center failures were classified into failures occurring inside the server (Outage A) and failures occurring outside the server (Outage B), and focused on analyzing complex failures occurring within the server. Server external failures include power, cooling, user errors, etc. Since such failures can be prevented in the early stages of data center facility construction, various solutions are being developed. On the other hand, the cause of the failure occurring in the server is difficult to determine, and adequate prevention has not yet been achieved. In particular, this is the reason why server failures do not occur singularly, cause other server failures, or receive something that causes failures from other servers. In other words, while the existing studies assumed that it was a single server that did not affect the servers and analyzed the failure, in this study, the failure occurred on the assumption that it had an effect between servers. In order to define the complex failure situation in the data center, failure history data for each equipment existing in the data center was used. There are four major failures considered in this study: Network Node Down, Server Down, Windows Activation Services Down, and Database Management System Service Down. The failures that occur for each device are sorted in chronological order, and when a failure occurs in a specific equipment, if a failure occurs in a specific equipment within 5 minutes from the time of occurrence, it is defined that the failure occurs simultaneously. After configuring the sequence for the devices that have failed at the same time, 5 devices that frequently occur simultaneously within the configured sequence were selected, and the case where the selected devices failed at the same time was confirmed through visualization. Since the server resource information collected for failure analysis is in units of time series and has flow, we used Long Short-term Memory (LSTM), a deep learning algorithm that can predict the next state through the previous state. In addition, unlike a single server, the Hierarchical Attention Network deep learning model structure was used in consideration of the fact that the level of multiple failures for each server is different. This algorithm is a method of increasing the prediction accuracy by giving weight to the server as the impact on the failure increases. The study began with defining the type of failure and selecting the analysis target. In the first experiment, the same collected data was assumed as a single server state and a multiple server state, and compared and analyzed. The second experiment improved the prediction accuracy in the case of a complex server by optimizing each server threshold. In the first experiment, which assumed each of a single server and multiple servers, in the case of a single server, it was predicted that three of the five servers did not have a failure even though the actual failure occurred. However, assuming multiple servers, all five servers were predicted to have failed. As a result of the experiment, the hypothesis that there is an effect between servers is proven. As a result of this study, it was confirmed that the prediction performance was superior when the multiple servers were assumed than when the single server was assumed. In particular, applying the Hierarchical Attention Network algorithm, assuming that the effects of each server will be different, played a role in improving the analysis effect. In addition, by applying a different threshold for each server, the prediction accuracy could be improved. This study showed that failures that are difficult to determine the cause can be predicted through historical data, and a model that can predict failures occurring in servers in data centers is presented. It is expected that the occurrence of disability can be prevented in advance using the results of this study.
This study was performed to investigate: (i) the bending processing properties of silk worm oak (Quercus acutissima Carr.) and Korean red pine (Pinus densiflora S. et Z.) by boiling and steaming treatments; (ii) the effects of interrelated factors - sapwood and heartwood, annual ring placement, softening temperature and time, moisture content. and wood defects on bending processing properties; (iii) the changing rates of bending radii after release from a tension strap, and (iv) the improving methods of bending process by treatment with chemicals. The size of specimens tested was
To obtain useful fundamental informations for improving cultural practices of barley, an investigation was made on the influences of different fertilizer level and seeding rate as well as seeding date on yield and yield components and their balancing procedure using barley variety Suwon # 18, and at the same time, 8 varieties including Suwon # 18 were also tested to clarify the varietal responses in terms of their yield and yield components under different seeding date at Crop Experiment Station, Suwon, during the period of 1969 and 1970. The results obtained were summarized as follows; 1. Days to emergence of barley variety Suwon # 18 at Suwon, took 8 to 19 days in accordance with given different seeding date (from Sept. 21 to Oct. 31). Earlier emergence was observed by early seeding and most of the seeds were emerged at 15